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Towards Smart Health Using Mobile Technologies

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Smart health is crucial in promoting individual well-being and long-term health outcomes. With the emerging sensing and computing power in pervasive mobile devices, smart health technologies can empower individuals to monitor their health metrics, track their physical activity, and make informed decisions about their lifestyle, forming a powerful synergy that fosters healthier lifestyles and prevents chronic illnesses in the future. We find that commercial WiFi signals can be exploited as a sensing modality in addition to its original communication usage, providing a contactless and low-cost solution for smart health in non-clinical environments. This talk will first introduce a personalized fitness assistant system that utilizes WiFi signals for effective exercise monitoring and assessment at a relatively coarse-grained level. The system leverages Channel State Information (CSI) measurements from existing WiFi infrastructure to provide workout statistics and dynamic evaluations. A Deep Neural Network (DNN) model is employed for workout recognition and individual identification tasks. The study investigates the impact of factors such as the sensitive region between WiFi transceivers and ambient interference on system performance. I will then take a deeper look of estimating fine-grained vital signs (e.g., breathing rate and heartbeats) during sleep using minute WiFi signal changes. Our approach demonstrates the feasibility of contactless, continuous, and fine-grained monitoring of vital signs without any additional cost. In addition, the system can distinguish different sleep events and track sleep postures to provide insights into sleep quality. We show that vital signs can be captured using only one AP and a single WiFi device, which can be extended to non-sleep scenarios. Extensive experiments in laboratory and nonclinical settings show comparable or better performance compared to existing sensor-based approaches. These smart health systems offer convenience and potential for various smart health application scenarios, benefiting users in maintaining their healthy daily routines.



Yingying (Jennifer) Chen is a Professor and Department Chair of Electrical and Computer Engineering (ECE) and Peter Cherasia Endowed Faculty Scholar at Rutgers University. She is the Associate Director of Wireless Information Network Laboratory (WINLAB). She also leads the Data Analysis and Information Security (DAISY) Lab. She is a Fellow of IEEE and a Fellow of National Academy of Inventors (NAI). She is also an ACM Distinguished Member. Her research interests include Applied Machine Learning in Mobile Computing and Sensing, Internet of Things (IoT), Security in AI/ML Systems, Smart Healthcare, and Deep Learning on Mobile Systems. She is a pioneer in RF/WiFi sensing, location systems, and mobile security. Before joining Rutgers, she was a tenured professor at Stevens Institute of Technology and had extensive industry experiences at Nokia (previously Lucent Technologies). She has published 3 books, 4 book chapters and 240+ journal articles and refereed conference papers. She is the recipient of seven Best Paper Awards in top ACM and IEEE conferences. She is the recipient of NSF CAREER Award and Google Faculty Research Award. She received NJ Inventors Hall of Fame Innovator Award and is also the recipient of IEEE Region 1 Technological Innovation in Academic Award. Her research has been supported by many funding agencies including NSF , NIH, ARO , DoD and AFRL and reported in numerous media outlets

including MIT Technology Review, CNN , Fox News Channel, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving/served on the editorial boards of IEEE Transactions on Mobile Computing (TMC), IEEE Transactions on Wireless Communications (TWireless), IEEE /ACM Transactions on Networking (ToN) and ACM Transactions on Privacy and Security (TOPS). For more information, please refer to her homepage at:

This talk is part of the Mobile and Wearable Health Seminar Series series.

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